ObjectiveBileaflet mitral valve prolapse (MVP) with either focal or diffuse myocardial fibrosis has been linked to ventricular arrhythmia and/or sudden cardiac arrest. Left ventricular (LV) mechanical dispersion by speckle-tracking echocardiography (STE) is a measure of heterogeneity of ventricular contraction previously associated with myocardial fibrosis. The aim of this study is to determine whether mechanical dispersion can identify MVP at higher arrhythmic risk.MethodsWe identified 32 consecutive arrhythmic MVPs (A-MVP) with a history of complex ventricular ectopy on Holter/event monitor (n=23) or defibrillator placement (n=9) along with 27 MVPs without arrhythmic complications (NA-MVP) and 39 controls. STE was performed to calculate global longitudinal strain (GLS) as the average peak longitudinal strain from an 18-segment LV model and mechanical dispersion as the SD of the time to peak strain of each segment.ResultsMVPs had significantly higher mechanical dispersion compared with controls (52 vs 42 ms, p=0.005) despite similar LV ejection fraction (62% vs 63%, p=0.42) and GLS (−19.7 vs −21, p=0.045). A-MVP and NA-MVP had similar demographics, LV ejection fraction and GLS (all p>0.05). A-MVP had more bileaflet prolapse (69% vs 44%, p=0.031) with a similar degree of mitral regurgitation (mostly trace or mild in both groups) (p>0.05). A-MVP exhibited greater mechanical dispersion when compared with NA-MVP (59 vs 43 ms, p=0.0002). Mechanical dispersion was the only significant predictor of arrhythmic risk on multivariate analysis (OR 1.1, 95% CI 1.02 to 1.11, p=0.006).ConclusionsSTE-derived mechanical dispersion may help identify MVP patients at higher arrhythmic risk.
Purpose Prior studies reporting efficacy of radiofrequency catheter ablation for complex ventricular ectopy in mitral valve prolapse (MVP) are limited by selective inclusion of bileaflet MVP, papillary muscle only ablation, or short-term follow-up. We sought to evaluate the long-term incidence of hemodynamically significant ventricular tachycardia (VT) or fibrillation (VF) in patients with MVP after initial ablation. Methods We studied consecutive patients with MVP undergoing ablation for complex ventricular ectopy between 2013 and 2017 at our institution. Of 580 patients with MVP, we included 15 (2.6%, 10 women; mean age 50 ± 14 years, 53% bileaflet) with complex ventricular ectopy treated with initial ablation. Results Over a median follow-up of 3406 (1875-6551) days or 9 years, 5 of 15 (33%) patients developed hemodynamically significant VT/VF after their initial ablation and underwent placement of an implantable cardioverter defibrillator (ICD). Three of 5 also underwent repeat ablations. Sustained VT was inducible prior to index ablation in all 5 who developed VT/VF, compared to none of the 10 patients who did not develop VT/VF after index ablation (p = 0.002). Complex ventricular ectopy at index ablation was multifocal in all 5 patients who underwent repeat intervention versus 4 of 10 patients (40%) who did not (p = 0.04). All 3 patients with subsequent VT/VF who underwent repeat ablation had a new clinically dominant focus of ventricular arrhythmia and 3 of the patients with ICD had appropriate VT/VF therapies. Conclusions In the long term, a subset of MVP patients treated with ablation for ventricular arrhythmias, all with multifocal ectopy on initial EP study, develop hemodynamically significant VT/VF. Our findings suggest the progressive nature of ventricular arrhythmias in patients with MVP and multifocal ectopy.
Background: Mitral valve prolapse (MVP) is a common valvulopathy, with a subset of MVP patients developing sudden cardiac death or cardiac arrest. Complex ventricular ectopy (ComVE) represents a marker of arrhythmic risk that is associated with myocardial fibrosis and increased mortality in MVP. We hypothesize that an ECG-based machine-learning model can identify MVP with ComVE and/or myocardial fibrosis on cardiac magnetic resonance (CMR) imaging. Methods: A deep convolutional neural network (CNN) was trained to detect ComVE using 6,916 12-lead ECGs from 569 MVP patients evaluated at the University of California San Francisco (UCSF) between 2012 and 2020. A separate CNN was also trained to detect late gadolinium enhancement (LGE) using 87 ECGs from MVP patients with contrast CMR. Results: The prevalence of ComVE was 160/569 or 28% (20 patients or 3% had cardiac arrest or sudden cardiac death). The area under the curve (AUC) of the CNN to detect ComVE was 0.81 (95% CI, 0.78-0.84). AUC remained high even after excluding patients with moderate-severe mitral regurgitation (MR) [0.80 (95% CI, 0.77-0.83)], or with bileaflet MVP [0.81 (95% CI, 0.76-0.85)]. The top ECG segments able to discriminate ComVE vs no ComVE were related to ventricular depolarization and repolarization (early-mid ST and QRS fromV1, V3, and III). LGE in the papillary muscles or basal inferolateral wall was present in 21 (24%) of 87 patients with available CMR. The AUC for detection of LGE was 0.75 (95% CI, 0.68-0.82). Conclusions: Standard 12-lead ECGs analyzed with machine learning can detect MVP at risk for ventricular arrhythmias and fibrosis and can identify novel ECG correlates of arrhythmic risk regardless of leaflet involvement or mitral regurgitation severity. ECG-based CNNs may help select those MVP patients requiring closer follow-up and/or a CMR.
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